Work / Profit Intelligence Platform

Profit Intelligence Platform for Consumer Brands

Case study note: All figures shown are illustrative and rounded for portfolio purposes. System architecture, methodology, and decisioning logic are represented as built; specific client financial data has been redacted under confidentiality.
AI Business Intelligence Data Analytics Product Design

Enterprise-grade profit visibility and decisioning infrastructure for direct-to-consumer brands.

RoleSolo · Product + Engineering
TimelineJan – Apr 2025
StackClaude API · JS · HTML/CSS · Shopify · GA4
StatusShipped
Most consumer brands sell out and celebrate. Then quarterly, they realize they made less than they should have.

Most direct-to-consumer brands track profit on a spreadsheet, if at all. They know revenue from their storefront, maybe costs from a bank statement, but the gap between "we sold out" and "we actually made money" is a black hole. I built a financial operating system that gives brand operators real-time visibility into every dollar — where it comes from, where it leaks, and what to fix before committing capital to the next drop.

Sold out doesn't mean profitable
$9,000
Total expenses
$1,000
Airway freight alone
$1,500
Printing costs above optimal

The brand sold out their drop completely — 200 units, 100% sell-through. By every vanity metric, it was a win. But when I audited the actual P&L, the picture was different: $12,000 in revenue, $9,000 in costs, leaving just $3,000 in profit. A 25% margin on a sold-out drop is a sign that costs are out of control, not that the business is healthy.

Auditing every line item

I went through every invoice, receipt, and storefront transaction from the drop. The goal was to categorize every dollar and find where costs were inflated relative to industry benchmarks and available alternatives.

CategoryActualOptimizedSavings
Blanks / core product cost$2,500$2,500$0
Printing / decoration$1,800$1,300$500
Freight (airway)$1,000$250$750
Packaging & fulfillment$800$800$0
Marketing$700$700$0
Rush shipping$500$0$500
Samples & giveaways$1,200$1,200$0
Platform & misc fees$500$500$0
Total$9,000$7,250$1,750
Key Finding
Airway freight cost roughly $5/unit. Sea freight for the same order would have been ~$1.25/unit — a 75% reduction. The brand shipped air because they ordered late, not because they needed speed.
Three leaks, one root cause
Airway Freight
$1,000 on a $3,000 order. Shipped air because of late planning.
-$1,000 fixable
Print Pricing
Volume discount missed. Screen-print threshold at ~150 units.
-$500 fixable
Rush Shipping
$500 in rush fees. Planning failure, not urgency.
-$500 preventable
Fix these three → profit goes from $3,000 to $5,000. A 1.7× improvement with zero new customers needed.
What I Built — The System

I designed and built five interconnected modules that give a brand operator complete financial visibility and forward-looking intelligence for every drop — turning unit economics into a leadership tool rather than a post-mortem.

Drag any input. Watch profit recalculate in real time.
This is the planner, running autonomously. Every slider re-runs the full P&L instantly, with revenue, costs, margin, and milestones all updated before a dollar of capital is committed.
DROP INPUTS
Total pieces
150
Top price ($)
70
Sell-through (%)
85
Sample pieces
15
Fulfillment ($/pc)
1.0
Giveaway pieces
0
P&L THIS DROP
Revenue
$8,925
Costs
$5,589
Profit
$3,336
Margin
37.4%
Break even56 pcsHit ✓
$1,000 net91 pcsHit ✓
$2,500 net142 pcsHit ✓
$5,000 net229 pcsMore
This tool is tailored to your business. Every brand has different economics: COGS, freight mode (sea / air), outbound shipping, fulfillment, platform fees, marketing mix, subscription services, samples, giveaways. I don't fit your numbers into a template; I rebuild the model around your cost structure. If a line item drives your margin, the planner models it.
1
Drop Planner
An interactive P&L calculator. Input unit count, cost per unit, retail prices, marketing spend, and samples. It outputs revenue, costs, profit, annual projection, and milestone markers (break-even, $1K, $2.5K, $5K profit thresholds) in real time — so decisions are instrumented before capital is committed.
2
Cost Bleed Analysis
Automatically flags cost categories that exceed industry benchmarks. Each leak shows the actual spend, what it should have been, and the fixable amount. The system calculates total recoverable profit and the improvement multiplier.
3
Pricing Intelligence
Compares current pricing against category benchmarks and calculates optimal price points based on cost structure and target margins.
Before
Product A $60 · Product B $55
Margin: 26.6%
Priced by gut feel
After
Product A $70 · Product B $65
Margin: 40%
+$2,000 per drop from pricing alone
4
AI Drop Intelligence Brief
A post-drop survey feeds into the Claude API to generate a comprehensive intelligence brief. The AI analyzes sell-through patterns, identifies cost optimization opportunities, and provides actionable recommendations for the next drop.
[ Post-Drop Survey ] → [ Claude API Analysis ] → [ Intelligence Brief with Recommendations ]
5
Customer Funnel Tracking
Connects GA4 and storefront data to map the full customer journey from first touch to purchase, identifying where potential buyers drop off.
01
Awareness
Social · Ads
02
Interest
Site visits
03
Cart
Add to cart
04
Checkout
Payment
05
Purchase
Confirmed
Measurable impact
$2,000
Recoverable per drop from cost fixes
+$2,000
Added revenue from pricing optimization
1.7×
Profit improvement multiplier
100%
Unit-level cost visibility

The platform shipped and is actively used for drop planning. The brand operator can now see the impact of every pricing and cost decision before committing capital to a production run, not after the drop is over. A 1.7× profit multiplier, zero new customers needed.

What I learned
Sold-out click data hides the real story
A 100% sell-through rate looks great on social, but it tells you nothing about profitability. The most important metric for a consumer brand isn't revenue — it's profit per unit after all costs. This project reinforced that dashboards should lead with profit, not revenue.
Unit economics compound at scale
Saving $5/unit on freight across 200 units is $1,000. Across 6 drops/year at higher volume, that's $10K+ annually from one fix. Small unit economics improvements create outsized returns when they repeat — which is why unit economics belongs on the leadership dashboard, not in the back office.
AI + real data beats AI alone
The Claude-powered intelligence briefs are only useful because they're grounded in actual transaction data, not hypotheticals. Feeding real cost breakdowns, sell-through rates, and customer funnel data into the prompt produces recommendations that are specific and actionable.